Comparison of ResNet50V2 and InceptionV3 with Adam, SGD, RMSprop Optimizers for Road Image Classification

Rifka Anrahvi (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Stevani Stevani (Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia)
Syahid Muhammad Hibban (Band?rma Onyedi Eylül Üniversitesi, Turkiye)

Indonesian Journal of Applied Technology and Innovation Science
Vol. 2 No. 2 (2025): IJATIS August 2025

This study compares two Convolutional Neural Network (CNN) architectures ResNet50V2 and InceptionV3  with three optimizers (Adam, RMSprop, and SGD) for road condition classification. Using a dataset of 1,000 images categorized into four classes, the models were evaluated based on accuracy, precision, recall, and F1-score. Based on the results, ResNet50V2 with Adam optimizer performed the best, achieving 99% accuracy, whereas SGD yielded less-than-ideal results. This study is interesting since it compares architecture–optimizer pairings, a topic that hasn't been extensively studied in other studies. The results offer useful information for creating automated and dependable road monitoring systems that facilitate effective infrastructure upkeep. To further enhance performance, future study might entail implementing regularization techniques and growing the dataset.